# Using Computational Approaches to Optimize Asthma Care Management

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2021 · $775,715

## Abstract

Abstract
 The study will develop more accurate, computational predictive models and a novel automatic explanation
function to better identify patients likely to benefit most from care management. For many chronic diseases, a
small portion of patients with high vulnerabilities, severe disease, or great barriers to care consume most
healthcare resources and costs. To improve outcomes and resource use, many healthcare systems use
predictive models to prospectively identify high-risk patients and enroll them in care management to implement
tailored care plans. For maximal benefit from costly care management with limited service capacity, only patients
at the highest risk should be enrolled. But, current patient identification approaches have two limitations:
1) Low prediction accuracy causes misclassification, wasted costs, and suboptimal care. If an existing model
 were used for care management allocation, enrollment would miss >50% of those who would benefit most
 but include others unlikely to benefit. A healthcare system often has insufficient data for model training and
 incomplete data on many patients. A typical model uses only a few risk factors for adverse outcomes, despite
 many being known. Also, many predictive variables on patient and system characteristics are not found yet.
2) No explanation of the reasons for a prediction causes poor adoption of the prediction and busy care
 managers to spend extra time and miss suitable interventions. Care managers need to understand why a
 patient is predicted to be at high risk before allocating to care management and forming a tailored care plan.
 Existing models rarely give such explanation, forcing care managers to do detailed patient chart reviews.
 To address the limitations and optimize care management for more high-risk patients to receive appropriate
care, the study will: a) improve accuracy of computationally identifying high-risk patients and assess potential
impact on outcomes; b) automate explanation of computational prediction results and assess impact on model
accuracy and outcomes; c) assess automatic explanations' impact on care managers' acceptance of the
predictions and perceived care plan quality. The use case will be asthma that affects 9% of Americans and incurs
439,000 hospitalizations, 1.8 million emergency room visits, and $56 billion in cost annually. Asthma experts and
computer scientists will use data from three leading healthcare systems; a novel, model-based transfer learning
technique needing no other system's raw data; a novel, pattern-based automatic explanation technique that also
improves model generalizability and accuracy; a new data source PreManage to make patient data more
complete; and novel features on patient and system characteristics. These techniques can advance clinical
machine learning for various applications, improve patient identification, and help form tailored care plans. Focus
groups will be conducted with clinicians to explore generalizing the t...

## Key facts

- **NIH application ID:** 10176558
- **Project number:** 5R01HL142503-04
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Gang Luo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $775,715
- **Award type:** 5
- **Project period:** 2018-08-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10176558

## Citation

> US National Institutes of Health, RePORTER application 10176558, Using Computational Approaches to Optimize Asthma Care Management (5R01HL142503-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10176558. Licensed CC0.

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